Time Series: Caiib Paper 1 (Module B), Unit 4
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So, here we are providing “Unit 4: Time Series” of “Module B: Business Mathematics” from “Paper 1: Advanced Bank Management (ABM)”.
The Article is Caiib Unit 4: Time Series
- Secular trend is caused by basic inherent factors. Business cycle trends are mostly upward. The quality of forecast depends on the information provided by past data and its validity. Data or statistical information accumulated at regular intervals is called TIME SERIES.
There are 4 types of variations in time series.
- Secular Trend
- Cyclical Fluctuation
- Seasonal Variation
- Irregular Variation.
- In this first type of variation the change comes over a long period of time. A steady increase in cost of living recorded by Consumer Price Index is a good example. From year to year there is a fluctuation but there is a steady increase in the trend. Let us see the series given here.
- Let us try to detect patterns in the information over regular intervals of time. Then let us try to predict to cope with uncertainty.
There is an increase over time of 7 years. But the increases are not equal.
- Most common example of a cyclical fluctuation is a business cycle. Over time, there are years when business cycle hits peak above the trend line. There are also times when business activity slumps, and hits a point below the trend line. Fluctuations in business activity occur many times, and they have irregular periods and vary widely in amplitude from cycle to cycle. The time between hitting peaks and lows are periods – it can be one or many. The cyclical moves do not follow any regular pattern, they are irregular.
- There is a pattern of change within a year. A doctor can expect the number of flu cases to increase in winter. Hill resorts can expect more tourists during summer. These are regular patterns and can be used for forecasting the amount of flu vaccines required during winter, the doctor’s income during winter, the hotel bookings in resorts and availability of air and train bookings.
- The value of the variable is unpredictable, changing in a random manner. The effects of earthquakes, floods, wars, etc., cannot be predicted.
- As a result of flood, the agriculture output suffers. Then the prices go up at an unprecedented rate. This could not be predicted by using time series.
- Even though we described time series as exhibiting one or another variation, in most instances real time series will contain several of these components. Then the question is how to measure them.
There are three main reasons, why we should study the trends:
- We will be able to describe historical patterns, which will help us to evaluate the success of previous policies – long-term direction of the time series is given by secular trend.
- Past trends will help us to project the future – some growth rate of population, GDP.
- We will be able to separate the trend component and eliminate it from the series, to get an accurate idea of other components like seasonal fluctuations.
- Cyclical variation is a component of the time series, which tends to oscillate above and below the secular trend line for periods longer than a year. Seasonal variation makes a complete regular cycle within each year and does not affect one year any more than another. Once we identify the secular trend, we can isolate the remaining cyclical and irregular components of the trend. Let us assume cyclical component explains most of the variations left unexplained by the trend analysis.
- Time series also includes seasonal variation. Seasonal variation is repetitive and predictable. This can be defined as movements around the trend line in one year or less. In order to measure seasonal variations, time intervals must be measured in small units, like days, weeks, etc.
- The final component is irregular variation. After we have eliminated trend, cyclical and seasonal variations from the time series, we may still have unpredictable factor left. Irregular variations occur over very short intervals and follow random patterns. We may not be able to isolate them mathematically, but we may isolate the causes for the same. For example, an unusually very cold winter in a region may increase electricity consumption significantly. Wars may increase air and train travel because of the movement of troops. We may not be able to identify all causes. But over time, these random variations tend to correct themselves.
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